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<a href="_gaussian_layer_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> * </span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \brief       -</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> *</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * \author      -</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * \date        -</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * </span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * </span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * </span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * </span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> *</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> */</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="preprocessor">#ifndef SHARK_UNSUPERVISED_RBM_NEURONLAYERS_GAUSSIANLAYER_H</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="preprocessor">#define SHARK_UNSUPERVISED_RBM_NEURONLAYERS_GAUSSIANLAYER_H</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span> </div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="preprocessor">#include &lt;<a class="code" href="_base_8h.html">shark/LinAlg/Base.h</a>&gt;</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="preprocessor">#include &lt;<a class="code" href="_real_space_8h.html">shark/Unsupervised/RBM/StateSpaces/RealSpace.h</a>&gt;</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#include &lt;<a class="code" href="_random_8h.html">shark/Core/Random.h</a>&gt;</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#include &lt;<a class="code" href="_i_serializable_8h.html">shark/Core/ISerializable.h</a>&gt;</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#include &lt;<a class="code" href="_i_parameterizable_8h.html">shark/Core/IParameterizable.h</a>&gt;</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="preprocessor">#include &lt;<a class="code" href="_math_8h.html">shark/Core/Math.h</a>&gt;</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_batch_interface_adapt_struct_8h.html">shark/Data/BatchInterfaceAdaptStruct.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="preprocessor">#include &lt;<a class="code" href="_open_m_p_8h.html">shark/Core/OpenMP.h</a>&gt;</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a>{</div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment"></span> </div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment">///\brief A layer of Gaussian neurons.</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="comment">///</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="comment">/// For a Gaussian neuron/variable the conditional probability distribution of the</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment">/// state of the variable given the state of the other layer is given by a Gaussian</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">/// distribution with the input of the neuron as mean and unit variance.</span></div>
<div class="foldopen" id="foldopen00048" data-start="{" data-end="};">
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html">   48</a></span><span class="comment"></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_gaussian_layer.html" title="A layer of Gaussian neurons.">GaussianLayer</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_i_serializable.html" title="Abstracts serializing functionality.">ISerializable</a>, <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_i_parameterizable.html" title="Top level interface for everything that holds parameters.">IParameterizable</a>&lt;&gt;{</div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>    RealVector m_bias; <span class="comment">///the bias terms associated with the neurons </span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="keyword">public</span>:<span class="comment"></span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">    ///the state space of this neuron is binary</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#a346b4d4edf91fa583578207488fc7fca">   53</a></span><span class="comment"></span>    <span class="keyword">typedef</span> <a class="code hl_struct" href="structshark_1_1_real_space.html" title="The RealSpace can&#39;t be enumerated. Infinite values are just too much.">RealSpace</a> <a class="code hl_typedef" href="classshark_1_1_gaussian_layer.html#a346b4d4edf91fa583578207488fc7fca" title="the bias terms associated with the neurons">StateSpace</a>;</div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment"></span> </div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">    ///\brief The sufficient statistics for the Guassian Layer stores the mean of the neuron and the inverse temperature</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#a4c79a6809fa54a8788fc9a73f9661759">   56</a></span><span class="comment"></span>    <span class="keyword">typedef</span> RealVector <a class="code hl_typedef" href="classshark_1_1_gaussian_layer.html#a4c79a6809fa54a8788fc9a73f9661759" title="The sufficient statistics for the Guassian Layer stores the mean of the neuron and the inverse temper...">SufficientStatistics</a>;<span class="comment"></span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">    ///\brief Sufficient statistics of a batch of data.</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95">   58</a></span><span class="comment"></span>    <span class="keyword">typedef</span> <a class="code hl_struct" href="structshark_1_1_batch.html" title="class which helps using different batch types">Batch&lt;SufficientStatistics&gt;::type</a> <a class="code hl_typedef" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95" title="Sufficient statistics of a batch of data.">StatisticsBatch</a>;</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>    <span class="comment"></span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">    /// \brief Returns the bias values of the units.</span></div>
<div class="foldopen" id="foldopen00061" data-start="{" data-end="}">
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#a877687dcf1eb19c17c0a2c941e7bf0a5">   61</a></span><span class="comment"></span>    <span class="keyword">const</span> RealVector&amp; <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#a877687dcf1eb19c17c0a2c941e7bf0a5" title="Returns the bias values of the units.">bias</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>        <span class="keywordflow">return</span> m_bias;</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>    }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">    /// \brief Returns the bias values of the units.</span></div>
<div class="foldopen" id="foldopen00065" data-start="{" data-end="}">
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#aaf729abb5e4ab69322d57f2a1b5fe39b">   65</a></span><span class="comment"></span>    RealVector&amp; <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#aaf729abb5e4ab69322d57f2a1b5fe39b" title="Returns the bias values of the units.">bias</a>(){</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>        <span class="keywordflow">return</span> m_bias;</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    }</div>
</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>        <span class="comment"></span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment">    ///\brief Resizes this neuron layer.</span></div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span><span class="comment">    ///@param newSize number of neurons in the layer</span></div>
<div class="foldopen" id="foldopen00072" data-start="{" data-end="}">
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#ad0d61deb12bd4455dbf6546d08328b72">   72</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad0d61deb12bd4455dbf6546d08328b72" title="Resizes this neuron layer.">resize</a>(std::size_t newSize){</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>        m_bias.resize(newSize);</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>    }</div>
</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>    <span class="comment"></span></div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span><span class="comment">    ///\brief Returns the number of neurons of this layer.</span></div>
<div class="foldopen" id="foldopen00077" data-start="{" data-end="}">
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1">   77</a></span><span class="comment"></span>    std::size_t <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1" title="Returns the number of neurons of this layer.">size</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>        <span class="keywordflow">return</span> m_bias.size();</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>    }</div>
</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>    <span class="comment"></span></div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span><span class="comment">    /// \brief Takes the input of the neuron and estimates the expectation of the response of the neuron.</span></div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span><span class="comment">    /// @param input the batch of inputs of the neuron</span></div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span><span class="comment">    /// @param statistics sufficient statistics containing the mean of the resulting Gaussian distribution</span></div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span><span class="comment">    /// @param beta the inverse Temperature of the RBM (typically 1) for the whole batch</span></div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Input, <span class="keyword">class</span> BetaVector&gt;</div>
<div class="foldopen" id="foldopen00087" data-start="{" data-end="}">
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#a0e768ac30ba61b25220fa17293591d0c">   87</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#a0e768ac30ba61b25220fa17293591d0c" title="Takes the input of the neuron and estimates the expectation of the response of the neuron.">sufficientStatistics</a>(Input <span class="keyword">const</span>&amp; input, <a class="code hl_typedef" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95" title="Sufficient statistics of a batch of data.">StatisticsBatch</a>&amp; statistics,BetaVector <span class="keyword">const</span>&amp; beta)<span class="keyword">const</span>{ <span class="comment">// \todo: auch hier noch mal namen ueberdenken</span></div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(input.size2() == <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1" title="Returns the number of neurons of this layer.">size</a>());</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(statistics.size2() == <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1" title="Returns the number of neurons of this layer.">size</a>());</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(input.size1() == statistics.size1());</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>        </div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != input.size1(); ++i){</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>            noalias(row(statistics,i)) = row(input,i)*beta(i)+m_bias;</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>        }</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>    }</div>
</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span> </div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span><span class="comment"></span> </div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span><span class="comment">    /// \brief Given a the precomputed statistics (the mean of the Gaussian), the elements of the vector are sampled.</span></div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span><span class="comment">    /// This happens either with Gibbs-Sampling or Flip-the-State sampling.</span></div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span><span class="comment">    /// For alpha= 0 gibbs sampling is performed. That is the next state for neuron i is directly taken from the conditional distribution of the i-th neuron. </span></div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span><span class="comment">    /// In the case of alpha=1, flip-the-state sampling is performed, which takes the last state into account and tries to do deterministically jump </span></div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span><span class="comment">    /// into states with higher probability. THIS IS NOT IMPLEMENTED YET and alpha is ignored!</span></div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span><span class="comment">    /// @param statistics sufficient statistics containing the mean of the conditional Gaussian distribution of the neurons</span></div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span><span class="comment">    /// @param state the state matrix that will hold the sampled states</span></div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="comment">    /// @param alpha factor changing from gibbs to flip-the state sampling. 0&lt;=alpha&lt;=1</span></div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span><span class="comment">    /// @param rng the random number generator used for sampling</span></div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Matrix, <span class="keyword">class</span> Rng&gt;</div>
<div class="foldopen" id="foldopen00110" data-start="{" data-end="}">
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#a11e7d48c8d1b8faf811d3870d9ce818a">  110</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#a11e7d48c8d1b8faf811d3870d9ce818a" title="Given a the precomputed statistics (the mean of the Gaussian), the elements of the vector are sampled...">sample</a>(<a class="code hl_typedef" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95" title="Sufficient statistics of a batch of data.">StatisticsBatch</a> <span class="keyword">const</span>&amp; statistics, Matrix&amp; state, <span class="keywordtype">double</span> alpha, Rng&amp; rng)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(statistics.size2() == <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1" title="Returns the number of neurons of this layer.">size</a>());</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(statistics.size1() == state.size1());</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(statistics.size2() == state.size2());</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        </div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>        <a class="code hl_define" href="_open_m_p_8h.html#a6de33df9d72bea69f903cffb391e7121">SHARK_CRITICAL_REGION</a>{</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>            <span class="keywordflow">for</span>(std::size_t i = 0; i != state.size1();++i){</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>                <span class="keywordflow">for</span>(std::size_t j = 0; j != state.size2();++j){</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>                    state(i,j) = <a class="code hl_function" href="namespaceshark_1_1random.html#a972c5f7f031612a130aa077fc9136a9f" title="Draws a number from the normal distribution with given mean and variance by drawing random numbers fr...">random::gauss</a>(rng,statistics(i,j), 1.0);</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>                }</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>            }</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        }</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        (void) alpha;</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>    }</div>
</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>    <span class="comment"></span></div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span><span class="comment">    /// \brief Computes the log of the probability of the given states in the conditional distribution</span></div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span><span class="comment">    /// Currently it is only possible to compute the case with alpha=0</span></div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment">    /// @param statistics the statistics of the conditional distribution</span></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span><span class="comment">    /// @param state the state to check</span></div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Matrix&gt;</div>
<div class="foldopen" id="foldopen00132" data-start="{" data-end="}">
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#a117e4b765db5d8b37c2446ae6451937e">  132</a></span>    RealVector <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#a117e4b765db5d8b37c2446ae6451937e" title="Computes the log of the probability of the given states in the conditional distribution.">logProbability</a>(<a class="code hl_typedef" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95" title="Sufficient statistics of a batch of data.">StatisticsBatch</a> <span class="keyword">const</span>&amp; statistics, Matrix <span class="keyword">const</span>&amp; state)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(statistics.size2() == <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1" title="Returns the number of neurons of this layer.">size</a>());</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(statistics.size1() == state.size1());</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(statistics.size2() == state.size2());</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        </div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        RealVector logProbabilities(state.size1(),1.0);</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        <span class="keywordflow">for</span>(std::size_t s = 0; s != state.size1();++s){</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>            <span class="keywordflow">for</span>(std::size_t i = 0; i != state.size2();++i){</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>                logProbabilities(s) -= 0.5*<a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(statistics(s,i)-state(s,i));</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>            }</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        }</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        <span class="keywordflow">return</span> logProbabilities;</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>    }</div>
</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span><span class="comment"></span> </div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span><span class="comment">    /// \brief Transforms the current state of the neurons for the multiplication with the weight matrix of the RBM,</span></div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span><span class="comment">    /// i.e. calculates the value of the phi-function used in the interaction term.</span></div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span><span class="comment">    /// In the case of Gaussian neurons the phi-function is just the identity.</span></div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span><span class="comment">    /// @param state the state matrix of the neuron layer</span></div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span><span class="comment">    /// @return the value of the phi-function</span></div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Matrix&gt;</div>
<div class="foldopen" id="foldopen00153" data-start="{" data-end="}">
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#ab7e9bdbf26ccea6190311b81ee4741cb">  153</a></span>    Matrix <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ab7e9bdbf26ccea6190311b81ee4741cb" title="Transforms the current state of the neurons for the multiplication with the weight matrix of the RBM,...">phi</a>(Matrix <span class="keyword">const</span>&amp; state)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(state.size2() == <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1" title="Returns the number of neurons of this layer.">size</a>());</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>        <span class="keywordflow">return</span> state;   </div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>    }</div>
</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span> </div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>    <span class="comment"></span></div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span><span class="comment">    /// \brief Returns the expectation of the phi-function. </span></div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span><span class="comment">    /// @param statistics the sufficient statistics (the mean of the distribution).</span></div>
<div class="foldopen" id="foldopen00161" data-start="{" data-end="}">
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#a9d8d02a951be66e02b06e21c9b646ab8">  161</a></span><span class="comment"></span>    RealMatrix <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#a9d8d02a951be66e02b06e21c9b646ab8" title="Returns the expectation of the phi-function.">expectedPhiValue</a>(<a class="code hl_typedef" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95" title="Sufficient statistics of a batch of data.">StatisticsBatch</a> <span class="keyword">const</span>&amp; statistics)<span class="keyword">const</span>{ </div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(statistics.size2() == <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1" title="Returns the number of neurons of this layer.">size</a>());</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        <span class="keywordflow">return</span> statistics;  </div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>    }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span><span class="comment">    /// \brief Returns the mean given the state of the connected layer, i.e. in this case the mean of the Gaussian</span></div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span><span class="comment">    /// </span></div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span><span class="comment">    /// @param statistics the sufficient statistics of the layer for a whole batch</span></div>
<div class="foldopen" id="foldopen00168" data-start="{" data-end="}">
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#ad8dfcc9b50d9bae82eaae02edc4a593b">  168</a></span><span class="comment"></span>    RealMatrix <span class="keyword">const</span>&amp; <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad8dfcc9b50d9bae82eaae02edc4a593b" title="Returns the mean given the state of the connected layer, i.e. in this case the mean of the Gaussian.">mean</a>(<a class="code hl_typedef" href="classshark_1_1_gaussian_layer.html#a510c15554b7b4bebec1c966e7ccaff95" title="Sufficient statistics of a batch of data.">StatisticsBatch</a> <span class="keyword">const</span>&amp; statistics)<span class="keyword">const</span>{ </div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(statistics.size2() == <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1" title="Returns the number of neurons of this layer.">size</a>());</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>        <span class="keywordflow">return</span> statistics;</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>    }</div>
</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span><span class="comment"></span> </div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span><span class="comment">    /// \brief The energy term this neuron adds to the energy function for a batch of inputs.</span></div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span><span class="comment">    /// @param state the state of the neuron layer</span></div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span><span class="comment">    /// @param beta the inverse temperature of the i-th state</span></div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span><span class="comment">    /// @return the energy term of the neuron layer</span></div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Matrix, <span class="keyword">class</span> BetaVector&gt;</div>
<div class="foldopen" id="foldopen00179" data-start="{" data-end="}">
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#ae920fde3c4ed485452214e90dabbadd4">  179</a></span>    RealVector <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ae920fde3c4ed485452214e90dabbadd4" title="The energy term this neuron adds to the energy function for a batch of inputs.">energyTerm</a>(Matrix <span class="keyword">const</span>&amp; state, BetaVector <span class="keyword">const</span>&amp; beta)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(state.size2() == <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1" title="Returns the number of neurons of this layer.">size</a>());</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(state.size1() == beta.size());</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>        <span class="comment">//the following code does for batches the equivalent thing to:</span></div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>        <span class="comment">//return beta * inner_prod(m_bias,state) - norm_sqr(state)/2.0;</span></div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>        </div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>        std::size_t <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a> = state.size1();</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>        RealVector energies = prod(state,m_bias);</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>        noalias(energies) *= beta;</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>; ++i){</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>            energies(i) -= norm_sqr(row(state,i))/2.0;</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>        }</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>        <span class="keywordflow">return</span> energies;</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>        </div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>    }</div>
</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>    </div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span><span class="comment"></span> </div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span><span class="comment">    ///\brief Sums over all possible values of the terms of the energy function which depend on the this layer and returns the logarithmic result.</span></div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span><span class="comment">    ///This function is called by Energy when the unnormalized marginal probability of the connected layer is to be computed. </span></div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span><span class="comment">    ///This function calculates the part which depends on the neurons which are to be marginalized out.</span></div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span><span class="comment">    ///(In the case of the binary hidden neuron, this is the term \f$  \log \sum_h e^{\vec h^T W \vec v+ \vec h^T \vec c} \f$). </span></div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span><span class="comment">    ///The rest is calculated by the energy function.</span></div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span><span class="comment">    ///In the general case of a hidden layer, this function calculates \f$ \log \int_h e^(\phi_h(\vec h)^T W \phi_v(\vec v)+f_h(\vec h) ) \f$ </span></div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span><span class="comment">    ///where f_h  is the energy term of this.</span></div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span><span class="comment">    /// @param inputs the inputs of the neurons they get from the other layer</span></div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span><span class="comment">    /// @param beta the inverse temperature of the RBM</span></div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span><span class="comment">    /// @return the marginal distribution of the connected layer</span></div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Input&gt;</div>
<div class="foldopen" id="foldopen00209" data-start="{" data-end="}">
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#ada4d3177d4f9f4befe18168f9281ae47">  209</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ada4d3177d4f9f4befe18168f9281ae47" title="Sums over all possible values of the terms of the energy function which depend on the this layer and ...">logMarginalize</a>(<span class="keyword">const</span> Input&amp; inputs, <span class="keywordtype">double</span> beta)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(inputs.size() == <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1" title="Returns the number of neurons of this layer.">size</a>());</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>        <span class="keywordtype">double</span> lnResult = 0;</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>        <span class="keywordtype">double</span> logNormalizationTerm = std::log(<a class="code hl_variable" href="group__shark__globals.html#ga49b759f4712477bf89276a6c944fec47" title="Constant for sqrt( 2 * pi ).">SQRT_2_PI</a>)  - 0.5 * std::log(beta);</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>        </div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1" title="Returns the number of neurons of this layer.">size</a>(); ++i){</div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>            lnResult += 0.5 * <a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(inputs(i)+m_bias(i))*beta;</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>            lnResult += logNormalizationTerm;</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>        }</div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>        <span class="keywordflow">return</span> lnResult;</div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>    }</div>
</div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span> </div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Vector, <span class="keyword">class</span> SampleBatch, <span class="keyword">class</span> Vector2 &gt;</div>
<div class="foldopen" id="foldopen00222" data-start="{" data-end="}">
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#a04bb5c1a6335d0d2b199804875c0ceb8">  222</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#a04bb5c1a6335d0d2b199804875c0ceb8">expectedParameterDerivative</a>(Vector&amp; derivative, SampleBatch <span class="keyword">const</span>&amp; samples, Vector2 <span class="keyword">const</span>&amp; weights )<span class="keyword">const</span>{</div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(derivative.size() == <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1" title="Returns the number of neurons of this layer.">size</a>());</div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span>        noalias(derivative) += prod(weights,samples.statistics);</div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>    }</div>
</div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span>    <span class="comment"></span></div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span><span class="comment">    ///\brief Calculates the derivatives of the energy term of this neuron layer with respect to it&#39;s parameters - the bias weights. </span></div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span><span class="comment">    ///This function takes a batch of samples and calculates a weighted derivative</span></div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span><span class="comment">    ///@param derivative the derivative with respect to the parameters, the result is added on top of it to accumulate derivatives</span></div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span><span class="comment">    ///@param samples the sample from which the informations can be extracted</span></div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span><span class="comment">    ///@param weights the weights for the single sample derivatives</span></div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span><span class="comment"></span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> Vector, <span class="keyword">class</span> SampleBatch, <span class="keyword">class</span> WeightVector&gt;</div>
<div class="foldopen" id="foldopen00234" data-start="{" data-end="}">
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#a0fb9583b9a3316795085314c93499e08">  234</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#a0fb9583b9a3316795085314c93499e08" title="Calculates the derivatives of the energy term of this neuron layer with respect to it&#39;s parameters - ...">parameterDerivative</a>(Vector&amp; derivative, SampleBatch <span class="keyword">const</span>&amp; samples, WeightVector <span class="keyword">const</span>&amp; weights)<span class="keyword">const</span>{</div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(derivative.size() == <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1" title="Returns the number of neurons of this layer.">size</a>());</div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>        noalias(derivative) += prod(weights,samples.state);</div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span>    }</div>
</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>    <span class="comment"></span></div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span><span class="comment">    ///\brief Returns the vector with the parameters associated with the neurons in the layer.</span></div>
<div class="foldopen" id="foldopen00240" data-start="{" data-end="}">
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#a4dbab4818dff8ee8011b94713513bf4f">  240</a></span><span class="comment"></span>    RealVector <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#a4dbab4818dff8ee8011b94713513bf4f" title="Returns the vector with the parameters associated with the neurons in the layer.">parameterVector</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>        <span class="keywordflow">return</span> m_bias;</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>    }</div>
</div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span><span class="comment"></span> </div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span><span class="comment">    ///\brief Returns the vector with the parameters associated with the neurons in the layer.</span></div>
<div class="foldopen" id="foldopen00245" data-start="{" data-end="}">
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#a9ec36fc2567fe4b55fb2b976baea9be4">  245</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#a9ec36fc2567fe4b55fb2b976baea9be4" title="Returns the vector with the parameters associated with the neurons in the layer.">setParameterVector</a>(RealVector <span class="keyword">const</span>&amp; newParameters){</div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>        m_bias = newParameters;</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>    }</div>
</div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span><span class="comment"></span> </div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span><span class="comment">    ///\brief Returns the number of the parameters associated with the neurons in the layer.</span></div>
<div class="foldopen" id="foldopen00250" data-start="{" data-end="}">
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#a4282e99234da5b7ee7de906abd2037cb">  250</a></span><span class="comment"></span>    std::size_t <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#a4282e99234da5b7ee7de906abd2037cb" title="Returns the number of the parameters associated with the neurons in the layer.">numberOfParameters</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>        <span class="keywordflow">return</span> <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#ad2a3749d604f6e425f069726cae281d1" title="Returns the number of neurons of this layer.">size</a>();</div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>    }</div>
</div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>    <span class="comment"></span></div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span><span class="comment">    /// \brief Reads the bias parameters from an archive.</span></div>
<div class="foldopen" id="foldopen00255" data-start="{" data-end="}">
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#a8c717e6c37de891dd44a1a4640d589a0">  255</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#a8c717e6c37de891dd44a1a4640d589a0" title="Reads the bias parameters from an archive.">read</a>( <a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a> &amp; archive ){</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>        archive &gt;&gt; m_bias;</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>    }<span class="comment"></span></div>
</div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span><span class="comment">    /// \brief Writes the bias parameters to an archive.</span></div>
<div class="foldopen" id="foldopen00259" data-start="{" data-end="}">
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"><a class="line" href="classshark_1_1_gaussian_layer.html#af6a8b46ec5a2bbef8c4fce3c17ddd565">  259</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_gaussian_layer.html#af6a8b46ec5a2bbef8c4fce3c17ddd565" title="Writes the bias parameters to an archive.">write</a>( <a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a> &amp; archive )<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>        archive &lt;&lt; m_bias;</div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>    }</div>
</div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>};</div>
</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span> </div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>}</div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span><span class="preprocessor">#endif</span></div>
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